class_docstr: Main coreference resolution algorithm
docstr: Return a list of utterrances text where the 
docstr_tok: ["Return","a","list","of","utterrances","text","where","the"]
code: def get_resolved_utterances(
      self, last_utterances_added=True, blacklist=True
  ):
      """Return a list of utterrances text where the"""
      coreferences = self.get_most_representative(
          last_utterances_added, blacklist
      )
      resolved_utterances = []
      for utt in self.get_utterances(
          last_utterances_added=last_utterances_added
      ):
          resolved_utt = ""
          in_coref = None
          for token in utt:
              if in_coref is None:
                  for (
                      coref_original,
                      coref_replace,
                  ) in coreferences.items():
                      if coref_original[0] == token:
                          in_coref = coref_original
                          resolved_utt += coref_replace.text.lower()
                          break
                  if in_coref is None:
                      resolved_utt += token.text_with_ws
              if in_coref is not None and token == in_coref[-1]:
                  resolved_utt += (
                      " "
                      if token.whitespace_
                      and resolved_utt[-1] is not " "
                      else ""
                  )
                  in_coref = None
          resolved_utterances.append(resolved_utt)
      return resolved_utterances
  
imports: import os
  import spacy
  import numpy
  from neuralcoref.train.utils import PACKAGE_DIRECTORY, SIZE_SINGLE_IN
  from neuralcoref.train.compat import unicode_
  from neuralcoref.train.document import Document, MENTION_TYPE, NO_COREF_LIST
label_desc: Use image node_id 11 for calling the Coref obj's underlying member method code with example usage: obj.get_resolved_utterances(last_utterances_added, blacklist) and returns: resolved_utterances